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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Likelihood functions in HSSM explained\n",
    "\n",
    "One of the design goals of HSSM is its flexibility. It is built from ground up to support many types of likelihood functions out-of-the-box. For more tailored applications, HSSM provides a convenient toolbox. This allows users to create their own likelihood functions, which can seamlessly integrate with the HSSM class, facilitating a highly customizable analysis environment. This notebook focuses on explaining how to use different types of likelihoods with HSSM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Colab Instructions\n",
    "\n",
    "If you would like to run this tutorial on Google colab, please click this [link](https://github.com/lnccbrown/HSSM/blob/main/docs/tutorials/likelihoods.ipynb). \n",
    "\n",
    "Once you are *in the colab*, follow the *installation instructions below* and then **restart your runtime**. \n",
    "\n",
    "Just **uncomment the code in the next code cell** and run it!\n",
    "\n",
    "**NOTE**:\n",
    "\n",
    "You may want to *switch your runtime* to have a GPU or TPU. To do so, go to *Runtime* > *Change runtime type* and select the desired hardware accelerator.\n",
    "\n",
    "Note that if you switch your runtime you have to follow the installation instructions again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install numpy==1.23.4\n",
    "# !pip install git+https://github.com/lnccbrown/hssm@main\n",
    "# !pip install git+https://github.com/brown-ccv/hddm-wfpt@main\n",
    "# !pip install numpyro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import arviz as az\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pytensor\n",
    "\n",
    "import hssm\n",
    "import ssms.basic_simulators\n",
    "\n",
    "pytensor.config.floatX = \"float32\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-simulate some data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.614005</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.921775</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.236034</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.206972</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.605005</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>1.362994</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0.932995</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2.917993</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1.310991</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1.277991</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           rt  response\n",
       "0    1.614005       1.0\n",
       "1    5.921775       1.0\n",
       "2    2.236034       1.0\n",
       "3    3.206972       1.0\n",
       "4    1.605005       1.0\n",
       "..        ...       ...\n",
       "995  1.362994       1.0\n",
       "996  0.932995       1.0\n",
       "997  2.917993       1.0\n",
       "998  1.310991       1.0\n",
       "999  1.277991      -1.0\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simulate some data\n",
    "v_true, a_true, z_true, t_true = [0.5, 1.5, 0.5, 0.3]\n",
    "obs_angle = ssms.basic_simulators.simulator(\n",
    "    [v_true, a_true, z_true, t_true], model=\"ddm\", n_samples=1000\n",
    ")\n",
    "obs_angle = np.column_stack([obs_angle[\"rts\"][:, 0], obs_angle[\"choices\"][:, 0]])\n",
    "data = pd.DataFrame(obs_angle, columns=[\"rt\", \"response\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Three Kinds of Likelihoods\n",
    "\n",
    "HSSM supports 3 kinds of likelihood functions supported via the `loglik_kind` parameter to the `HSSM` class:\n",
    "\n",
    "- `\"analytical\"`: These likelihoods are usually closed-form solutions to the actual likelihoods. For example, For `ddm` models, HSSM provides the analytical likelihoods in [Navarro & Fuss (2009)](https://psycnet.apa.org/record/2009-11068-003). HSSM expects these functions to be written with `pytensor`, which can be compiled by `pytensor` as part of a computational graph. As such, they are differentiable as well.\n",
    "- `\"approx_differentiable\"`: These likelihoods are usually approximations of the actual likelihood functions with neural networks. These networks can be trained with any popular deep learning framework such as `PyTorch` and `TensorFlow` and saved as `onnx` files. HSSM can load the `onnx` files and translate the information of the neural network with either the `jax` or the `pytensor` backends. Please see below for detailed explanations for these backends. The `backend` option can be supplied via the `\"backend\"` field in `model_config`. This field of `model_config` is not applicable to other kinds of likelihoods.\n",
    "\n",
    "  - the `jax` backend: The basic computations in the likelihood are jax operations (valid `JAX` functions), which are wrapped in a `pytensor` `Op`. When sampling using the default NUTS sampler in `PyMC`, this option might be slightly faster but more prone to compatibility issues especially during parallel sampling due how `JAX` handles paralellism.The preferred usage of this backend is together with the `nuts_numpyro` and `black_jax` (experimental) samplers. Here JAX support is native and performance is optimized.\n",
    "  - the `pytensor` backend: The basic computations in the likelihood are pytensor operations (valid `pytensor` functions). When sampling using the default NUTS sampler in `PyMC`, this option allows for maximum compatibility. Not recommended when using `JAX`-based samplers.\n",
    "\n",
    "- `\"blackbox\"`: Use this option for \"black box\" likelihoods that are not differentiable. These likelihoods are typically `Callable`s in Python that cannot be directly integrated to a `pytensor` computational graph. `hssm` will wrap these `Callable`s in a `pytensor` `Op` so it can be part of the graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Default vs. Custom Likelihoods\n",
    "\n",
    "HSSM provides many default likelihood functions out-of-the-box. The supported likelihoods are:\n",
    "\n",
    "- For `analytical` kind: `ddm` and `ddm_sdv` models.\n",
    "- For `approx_differentiable` kind: `ddm`, `ddm_sdv`, `angle`, `levy`, `ornstein`, `weibull`, `race_no_bias_angle_4` and `ddm_seq2_no_bias`.\n",
    "\n",
    "For a model that has default likelihood functions, only the `model` argument needs to be specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddm_model_analytical = hssm.HSSM(data, model=\"ddm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hierarchical Sequential Sampling Model\n",
       "Model: ddm\n",
       "\n",
       "Response variable: rt,response\n",
       "Likelihood: analytical\n",
       "Observations: 1000\n",
       "\n",
       "Parameters:\n",
       "\n",
       "v:\n",
       "    Prior: Uniform(lower: -10.0, upper: 10.0)\n",
       "    Explicit bounds: None\n",
       "a:\n",
       "    Prior: HalfNormal(sigma: 2.0)\n",
       "    Explicit bounds: None\n",
       "z:\n",
       "    Prior: Uniform(lower: 0.0, upper: 1.0)\n",
       "    Explicit bounds: (0.0, 1.0)\n",
       "t:\n",
       "    Prior: Uniform(lower: 0.0, upper: 2.0, initval: 0.10000000149011612)\n",
       "    Explicit bounds: None"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddm_model_analytical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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   "source": [
    "ddm_model_analytical.graph()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ddm` and `ddm_sdv` models have `analytical` and `approx_differentiable` likelihoods. If `loglik_kind` is not specified, the `analytical` likelihood will be used. We can however directly specify the `loglik_kind` argument for a given model, and if available, the likelihood backend will be switched automatically. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddm_model_approx_diff = hssm.HSSM(\n",
    "    data, model=\"ddm\", loglik_kind=\"approx_differentiable\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the model graph looks the same:"
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  },
  {
   "cell_type": "code",
   "execution_count": 10,
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     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "ddm_model_approx_diff.graph()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check that the likelihood is now coming from a different backend by printing the model string:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hierarchical Sequential Sampling Model\n",
       "Model: ddm\n",
       "\n",
       "Response variable: rt,response\n",
       "Likelihood: approx_differentiable\n",
       "Observations: 1000\n",
       "\n",
       "Parameters:\n",
       "\n",
       "v:\n",
       "    Prior: Uniform(lower: -3.0, upper: 3.0)\n",
       "    Explicit bounds: (-3.0, 3.0)\n",
       "a:\n",
       "    Prior: Uniform(lower: 0.30000001192092896, upper: 2.5)\n",
       "    Explicit bounds: (0.3, 2.5)\n",
       "z:\n",
       "    Prior: Uniform(lower: 0.0, upper: 1.0)\n",
       "    Explicit bounds: (0.0, 1.0)\n",
       "t:\n",
       "    Prior: Uniform(lower: 0.0, upper: 2.0)\n",
       "    Explicit bounds: (0.0, 2.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddm_model_approx_diff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how under the *Likelihood* rubric, it now says \"approx_differentiable\". Another simple way to check this is to access the `loglik_kind` attribute of our HSSM model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'approx_differentiable'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddm_model_approx_diff.loglik_kind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overriding default likelihoods\n",
    "\n",
    "Sometimes a likelihood other than the default version is preferred. In that case, you can supply a likelihood function directly to the `loglik` parameter. We will discuss acceptable likelihood function types in a moment. \n",
    "\n",
    "For illustration we load the basic analytical DDM likelihood, which is shipped with HSSM and supply it manually our HSSM model class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hssm.likelihoods.analytical import logp_ddm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddm_model_analytical_override = hssm.HSSM(\n",
    "    data, model=\"ddm\", loglik_kind=\"analytical\", loglik=logp_ddm\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HSSM automatically constructed our model with the likelihood function we provided. We can now take posterior samples as usual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (2 chains in 4 jobs)\n",
      "NUTS: [t, a, z, v]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 2 chains for 500 tune and 500 draw iterations (1_000 + 1_000 draws total) took 56 seconds.\n",
      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
     ]
    },
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       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (chain: 2, draw: 500)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1\n",
       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n",
       "Data variables:\n",
       "    t        (chain, draw) float32 0.1142 0.1142 0.1142 ... 0.2698 0.2819 0.3298\n",
       "    a        (chain, draw) float32 37.81 38.11 38.19 38.26 ... 1.521 1.578 1.493\n",
       "    z        (chain, draw) float32 0.2939 0.2939 0.2939 ... 0.4732 0.4827 0.5065\n",
       "    v        (chain, draw) float32 3.727 3.727 3.727 ... 0.5051 0.5655 0.4948\n",
       "Attributes:\n",
       "    created_at:                  2023-07-12T14:52:02.697998\n",
       "    arviz_version:               0.14.0\n",
       "    inference_library:           pymc\n",
       "    inference_library_version:   5.5.0\n",
       "    sampling_time:               55.89794373512268\n",
       "    tuning_steps:                500\n",
       "    modeling_interface:          bambi\n",
       "    modeling_interface_version:  0.11.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-78efc10a-aeea-4ca0-bb32-572b5e5e3736' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-78efc10a-aeea-4ca0-bb32-572b5e5e3736' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 2</li><li><span class='xr-has-index'>draw</span>: 500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e491d9b6-828f-4c6a-a5fa-d165bf412c15' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e491d9b6-828f-4c6a-a5fa-d165bf412c15' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-e4b7c9ad-69b5-473c-aec0-2fe89897c99d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4b7c9ad-69b5-473c-aec0-2fe89897c99d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1c70f5e-7c32-4b22-97d3-53b583ec3da4' class='xr-var-data-in' type='checkbox'><label for='data-f1c70f5e-7c32-4b22-97d3-53b583ec3da4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-1df412a2-8c4e-460f-839e-a6d442c0a320' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1df412a2-8c4e-460f-839e-a6d442c0a320' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22bc5fbe-d871-45b3-aafd-81ba5be87c7f' class='xr-var-data-in' type='checkbox'><label for='data-22bc5fbe-d871-45b3-aafd-81ba5be87c7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d0256511-ea16-492b-85ab-301463e1e476' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0256511-ea16-492b-85ab-301463e1e476' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.1142 0.1142 ... 0.2819 0.3298</div><input id='attrs-f9302446-c4aa-4d97-aef8-6bd0ee72040e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9302446-c4aa-4d97-aef8-6bd0ee72040e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76e09b91-53e7-4e32-a8c3-1bab14720ed7' class='xr-var-data-in' type='checkbox'><label for='data-76e09b91-53e7-4e32-a8c3-1bab14720ed7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "        0.11423472, 0.11423472, 0.11423472, 0.11423472, 0.11423472,\n",
       "...\n",
       "        0.2782912 , 0.30404383, 0.2652966 , 0.24900784, 0.3248531 ,\n",
       "        0.29831055, 0.30316085, 0.2698118 , 0.27065778, 0.24444644,\n",
       "        0.29848057, 0.31757632, 0.32075304, 0.2613963 , 0.28917733,\n",
       "        0.28917733, 0.29565215, 0.26329637, 0.29887   , 0.2743925 ,\n",
       "        0.2898922 , 0.29146338, 0.26699564, 0.24125144, 0.2912997 ,\n",
       "        0.29326537, 0.29193285, 0.28381136, 0.3085205 , 0.2927997 ,\n",
       "        0.23638764, 0.32610247, 0.24979028, 0.27919817, 0.30304882,\n",
       "        0.29289353, 0.2842283 , 0.29903817, 0.31672367, 0.29535255,\n",
       "        0.23756932, 0.27805272, 0.3089128 , 0.3229872 , 0.28064114,\n",
       "        0.27591515, 0.26239163, 0.26745924, 0.26908246, 0.2788688 ,\n",
       "        0.26151264, 0.27195758, 0.24299578, 0.25586239, 0.3190421 ,\n",
       "        0.2514976 , 0.24113017, 0.25754577, 0.2621001 , 0.31128246,\n",
       "        0.2553485 , 0.28723723, 0.25909173, 0.30088377, 0.29093596,\n",
       "        0.27827838, 0.29128602, 0.30149022, 0.25704104, 0.27720532,\n",
       "        0.28972533, 0.29048377, 0.27153924, 0.27666515, 0.28615567,\n",
       "        0.3118053 , 0.23893082, 0.31363538, 0.3016433 , 0.30936465,\n",
       "        0.28913322, 0.2810567 , 0.2928814 , 0.2839218 , 0.2805744 ,\n",
       "        0.30217427, 0.29742715, 0.28154582, 0.34561282, 0.34561282,\n",
       "        0.27549395, 0.27719077, 0.2697735 , 0.28191283, 0.32984683]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>a</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>37.81 38.11 38.19 ... 1.578 1.493</div><input id='attrs-90d6cf8a-724e-45d4-9603-68107b442e1b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90d6cf8a-724e-45d4-9603-68107b442e1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdca18d7-5c44-48b4-8479-466c2c78cb13' class='xr-var-data-in' type='checkbox'><label for='data-bdca18d7-5c44-48b4-8479-466c2c78cb13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[37.805347 , 38.11459  , 38.190487 , 38.259277 , 38.259277 ,\n",
       "        38.559643 , 38.559643 , 38.641273 , 38.728985 , 38.81178  ,\n",
       "        38.898296 , 38.898296 , 38.97684  , 39.053684 , 39.137753 ,\n",
       "        39.220478 , 39.30403  , 39.30403  , 39.384186 , 39.465363 ,\n",
       "        39.558445 , 39.64288  , 39.722733 , 39.80601  , 39.80601  ,\n",
       "        39.897926 , 39.897926 , 39.967064 , 40.061924 , 40.145576 ,\n",
       "        40.236053 , 40.236053 , 40.313705 , 40.39592  , 40.481373 ,\n",
       "        40.5671   , 40.647835 , 40.731846 , 40.816635 , 40.904957 ,\n",
       "        40.99267  , 41.083633 , 41.083633 , 41.173687 , 41.251816 ,\n",
       "        41.344063 , 41.710583 , 41.781548 , 41.85999  , 42.231583 ,\n",
       "        42.327415 , 42.425488 , 42.425488 , 42.516705 , 42.609035 ,\n",
       "        42.712303 , 42.811993 , 43.209995 , 43.3135   , 43.3135   ,\n",
       "        43.701675 , 43.799236 , 43.799236 , 43.799236 , 43.904045 ,\n",
       "        44.00782  , 44.00782  , 44.11537  , 44.22275  , 44.332985 ,\n",
       "        44.44484  , 44.44484  , 44.44484  , 44.545204 , 44.65393  ,\n",
       "        44.65393  , 44.76191  , 44.869858 , 44.869858 , 44.97707  ,\n",
       "        45.084347 , 45.189205 , 45.311733 , 45.311733 , 45.311733 ,\n",
       "        45.426987 , 45.426987 , 45.426987 , 45.533966 , 45.643745 ,\n",
       "        45.760868 , 45.760868 , 45.760868 , 45.86447  , 46.324036 ,\n",
       "        46.433372 , 46.433372 , 46.551567 , 46.66284  , 46.770126 ,\n",
       "...\n",
       "         1.512142 ,  1.51979  ,  1.5224469,  1.585081 ,  1.5285238,\n",
       "         1.5125439,  1.5100461,  1.5527298,  1.5691233,  1.5261452,\n",
       "         1.5259454,  1.5161394,  1.5106485,  1.5377332,  1.5539186,\n",
       "         1.5539186,  1.5225548,  1.5508641,  1.499727 ,  1.5536282,\n",
       "         1.5284544,  1.5136493,  1.5786428,  1.5988426,  1.5062239,\n",
       "         1.5194805,  1.5066187,  1.536869 ,  1.5468681,  1.5238096,\n",
       "         1.5718919,  1.4979981,  1.5426484,  1.5654043,  1.5334663,\n",
       "         1.4978426,  1.523077 ,  1.5139449,  1.514242 ,  1.4621824,\n",
       "         1.576266 ,  1.5651882,  1.4935693,  1.5190076,  1.534364 ,\n",
       "         1.517608 ,  1.5435939,  1.5306693,  1.5370435,  1.5312212,\n",
       "         1.5433905,  1.5359621,  1.559609 ,  1.570575 ,  1.5027441,\n",
       "         1.5516446,  1.5595839,  1.5342673,  1.5406297,  1.4947901,\n",
       "         1.5526942,  1.541753 ,  1.5729254,  1.5389361,  1.5376128,\n",
       "         1.5398172,  1.5294907,  1.5260863,  1.5489652,  1.5382599,\n",
       "         1.5122858,  1.5325879,  1.5122244,  1.5520046,  1.5030122,\n",
       "         1.4891039,  1.591569 ,  1.5104262,  1.4924226,  1.5535545,\n",
       "         1.5053663,  1.5143467,  1.5398328,  1.5162365,  1.5565815,\n",
       "         1.4963115,  1.5072404,  1.5078807,  1.5004736,  1.5004736,\n",
       "         1.5476991,  1.5258952,  1.5207918,  1.5784458,  1.4933969]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.2939 0.2939 ... 0.4827 0.5065</div><input id='attrs-7e3ccc50-cba0-48ee-be54-2cfa3d9d4fcf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e3ccc50-cba0-48ee-be54-2cfa3d9d4fcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-303bbabb-bc40-4564-81af-d37f1554a2a2' class='xr-var-data-in' type='checkbox'><label for='data-303bbabb-bc40-4564-81af-d37f1554a2a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "        0.29387665, 0.29387665, 0.29387665, 0.29387665, 0.29387665,\n",
       "...\n",
       "        0.49014878, 0.4588903 , 0.487025  , 0.46782076, 0.5022351 ,\n",
       "        0.49174413, 0.49391845, 0.4888072 , 0.48284304, 0.48232678,\n",
       "        0.47741872, 0.49272674, 0.4888642 , 0.48908615, 0.4850924 ,\n",
       "        0.4850924 , 0.48154852, 0.49919546, 0.50012136, 0.4690266 ,\n",
       "        0.48969245, 0.50230694, 0.50023854, 0.48183662, 0.48124874,\n",
       "        0.47904485, 0.4910471 , 0.47960806, 0.4810369 , 0.48540005,\n",
       "        0.4851023 , 0.5050511 , 0.4903531 , 0.5033296 , 0.49472702,\n",
       "        0.47516668, 0.47818738, 0.4819806 , 0.4874509 , 0.48531076,\n",
       "        0.47241697, 0.47379187, 0.5185465 , 0.5154634 , 0.5023007 ,\n",
       "        0.47681266, 0.46718886, 0.48455656, 0.50165504, 0.48066926,\n",
       "        0.46787483, 0.48989597, 0.47545975, 0.49517685, 0.49763846,\n",
       "        0.47396812, 0.4894633 , 0.47926226, 0.48306948, 0.4994306 ,\n",
       "        0.49563766, 0.49608225, 0.50464565, 0.49592084, 0.49565536,\n",
       "        0.47212103, 0.49251488, 0.5022933 , 0.47616586, 0.49399105,\n",
       "        0.4811444 , 0.480732  , 0.5005957 , 0.48452783, 0.49165583,\n",
       "        0.4802631 , 0.48773915, 0.49099344, 0.51904476, 0.49655992,\n",
       "        0.5010509 , 0.4868067 , 0.48257905, 0.47910136, 0.49414188,\n",
       "        0.4923219 , 0.49073648, 0.4992084 , 0.52099663, 0.52099663,\n",
       "        0.46035787, 0.46613103, 0.4732239 , 0.48273057, 0.506532  ]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.727 3.727 3.727 ... 0.5655 0.4948</div><input id='attrs-75b9dfd7-05a7-4de4-a0e6-d542040e83aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75b9dfd7-05a7-4de4-a0e6-d542040e83aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2c1b3d4-9631-43e3-a319-52ced2a1aafa' class='xr-var-data-in' type='checkbox'><label for='data-c2c1b3d4-9631-43e3-a319-52ced2a1aafa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "        3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 , 3.7273016 ,\n",
       "...\n",
       "        0.5155268 , 0.54013157, 0.5728016 , 0.49688244, 0.49644375,\n",
       "        0.49703693, 0.4929886 , 0.51667213, 0.5070019 , 0.50370026,\n",
       "        0.49888134, 0.50458145, 0.5234709 , 0.4879074 , 0.53525925,\n",
       "        0.53525925, 0.52164936, 0.49634743, 0.49143028, 0.52478886,\n",
       "        0.4722252 , 0.48822784, 0.49784565, 0.5356035 , 0.5190897 ,\n",
       "        0.5269995 , 0.52239037, 0.49652576, 0.49861813, 0.54182625,\n",
       "        0.5365715 , 0.51985836, 0.46427822, 0.49951172, 0.5278673 ,\n",
       "        0.51549816, 0.5306759 , 0.5302563 , 0.5066967 , 0.5121746 ,\n",
       "        0.48898602, 0.5540371 , 0.44906998, 0.42651653, 0.50330925,\n",
       "        0.5337515 , 0.52447796, 0.5475912 , 0.50280094, 0.5725527 ,\n",
       "        0.49424934, 0.53258705, 0.5104151 , 0.53070545, 0.46173   ,\n",
       "        0.5517559 , 0.5750179 , 0.5319042 , 0.5194626 , 0.5137806 ,\n",
       "        0.49206924, 0.48231506, 0.48709106, 0.5017042 , 0.47447968,\n",
       "        0.5389004 , 0.52325153, 0.5155096 , 0.48639488, 0.5181265 ,\n",
       "        0.5272198 , 0.531271  , 0.55630684, 0.5541935 , 0.47824   ,\n",
       "        0.5029478 , 0.5281906 , 0.51410866, 0.47361374, 0.470376  ,\n",
       "        0.52907944, 0.5019655 , 0.54314613, 0.5251732 , 0.512928  ,\n",
       "        0.51527023, 0.49180222, 0.44715977, 0.45664024, 0.45664024,\n",
       "        0.6146984 , 0.54552174, 0.5051193 , 0.565464  , 0.49480915]],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-020ca165-3e4a-4414-ad80-2a247c7eef55' class='xr-section-summary-in' type='checkbox'  ><label for='section-020ca165-3e4a-4414-ad80-2a247c7eef55' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2aa52e77-688b-4638-8850-874d5295aa22' class='xr-index-data-in' type='checkbox'/><label for='index-2aa52e77-688b-4638-8850-874d5295aa22' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8d755135-3ebb-4e8a-9649-b38507ea90e6' class='xr-index-data-in' type='checkbox'/><label for='index-8d755135-3ebb-4e8a-9649-b38507ea90e6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef50cdb4-dab0-41d3-a61b-b2604cd66682' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ef50cdb4-dab0-41d3-a61b-b2604cd66682' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-07-12T14:52:02.697998</dd><dt><span>arviz_version :</span></dt><dd>0.14.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.5.0</dd><dt><span>sampling_time :</span></dt><dd>55.89794373512268</dd><dt><span>tuning_steps :</span></dt><dd>500</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.11.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_stats718b3d76-3240-4088-b969-35a8d8ff9734\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
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       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "  --xr-background-color-row-even: #111111;\n",
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       "\n",
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       "  max-width: 700px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
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       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
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       "\n",
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       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
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       "\n",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
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       "\n",
       ".xr-section-item {\n",
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       "\n",
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       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
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       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-details {\n",
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
       ".xr-preview {\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "\n",
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       "  padding: 0 !important;\n",
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       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       ".xr-attrs dt:hover {\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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       "\n",
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       "\n",
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       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
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       "\n",
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       ".xr-var-attrs,\n",
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       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
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       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (chain: 2, draw: 500)\n",
       "Coordinates:\n",
       "  * chain                  (chain) int64 0 1\n",
       "  * draw                   (draw) int64 0 1 2 3 4 5 ... 494 495 496 497 498 499\n",
       "Data variables: (12/17)\n",
       "    perf_counter_start     (chain, draw) float64 2.39e+05 2.39e+05 ... 2.391e+05\n",
       "    step_size_bar          (chain, draw) float64 0.001054 0.001054 ... 0.6265\n",
       "    index_in_trajectory    (chain, draw) int64 0 -2 1 -1 0 -2 ... -6 -2 -1 4 -6\n",
       "    acceptance_rate        (chain, draw) float64 0.7339 0.3694 ... 0.9166 0.8733\n",
       "    energy_error           (chain, draw) float64 0.0 1.203 ... 0.2325 -0.3685\n",
       "    largest_eigval         (chain, draw) float64 nan nan nan nan ... nan nan nan\n",
       "    ...                     ...\n",
       "    reached_max_treedepth  (chain, draw) bool False False False ... False False\n",
       "    step_size              (chain, draw) float64 0.0008649 0.0008649 ... 0.6154\n",
       "    lp                     (chain, draw) float64 1.261e+05 ... -2.019e+03\n",
       "    diverging              (chain, draw) bool False False False ... False False\n",
       "    n_steps                (chain, draw) float64 1.0 3.0 1.0 ... 7.0 7.0 7.0\n",
       "    smallest_eigval        (chain, draw) float64 nan nan nan nan ... nan nan nan\n",
       "Attributes:\n",
       "    created_at:                  2023-07-12T14:52:02.733467\n",
       "    arviz_version:               0.14.0\n",
       "    inference_library:           pymc\n",
       "    inference_library_version:   5.5.0\n",
       "    sampling_time:               55.89794373512268\n",
       "    tuning_steps:                500\n",
       "    modeling_interface:          bambi\n",
       "    modeling_interface_version:  0.11.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ba9231bf-66df-4491-a780-9604a8d253b7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ba9231bf-66df-4491-a780-9604a8d253b7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 2</li><li><span class='xr-has-index'>draw</span>: 500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-611b289a-f3b1-4118-bc51-e46c455fe45f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-611b289a-f3b1-4118-bc51-e46c455fe45f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-2e59d30e-caf9-4772-aa83-a39e00e95447' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e59d30e-caf9-4772-aa83-a39e00e95447' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcefc9db-d2ed-4e81-9141-ac327ce765db' class='xr-var-data-in' type='checkbox'><label for='data-dcefc9db-d2ed-4e81-9141-ac327ce765db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 495 496 497 498 499</div><input id='attrs-0c631c03-8990-4f73-8aac-275a05438cc6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c631c03-8990-4f73-8aac-275a05438cc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a28c238-d18b-4334-b50b-9192a8f9fffa' class='xr-var-data-in' type='checkbox'><label for='data-0a28c238-d18b-4334-b50b-9192a8f9fffa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 497, 498, 499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-755a31c3-e578-4eb8-8a95-55c43a3ae828' class='xr-section-summary-in' type='checkbox'  ><label for='section-755a31c3-e578-4eb8-8a95-55c43a3ae828' class='xr-section-summary' >Data variables: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_start</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.39e+05 2.39e+05 ... 2.391e+05</div><input id='attrs-afa12100-acf7-4ee6-92e1-c69a5103e52c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-afa12100-acf7-4ee6-92e1-c69a5103e52c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b3f88af-1b33-4e92-ad6e-e07f4e83b8ae' class='xr-var-data-in' type='checkbox'><label for='data-2b3f88af-1b33-4e92-ad6e-e07f4e83b8ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[239037.60145024, 239037.60718382, 239037.61949758,\n",
       "        239037.62601123, 239037.71409471, 239037.71953823,\n",
       "        239037.73173879, 239037.73754642, 239037.75485738,\n",
       "        239037.77016388, 239037.78392619, 239037.86854753,\n",
       "        239037.87709393, 239037.88715213, 239037.94165116,\n",
       "        239037.99934553, 239038.02570883, 239038.0499655 ,\n",
       "        239038.06358842, 239038.06954896, 239038.08128378,\n",
       "        239038.09185942, 239038.10278183, 239038.12574348,\n",
       "        239038.13173648, 239038.1803257 , 239038.20243783,\n",
       "        239038.20996115, 239038.21566821, 239038.23825532,\n",
       "        239038.24402538, 239038.25570334, 239038.26668949,\n",
       "        239038.2736826 , 239038.27941887, 239038.28506038,\n",
       "        239038.30663541, 239038.31237529, 239038.31817985,\n",
       "        239038.32959688, 239038.33810155, 239038.34645937,\n",
       "        239038.36309036, 239038.37114978, 239038.38641296,\n",
       "        239038.39421494, 239038.40864699, 239038.42264137,\n",
       "        239038.43133083, 239038.43858501, 239038.4998483 ,\n",
       "        239038.50659979, 239038.51961807, 239038.5427641 ,\n",
       "        239038.54922493, 239038.55582779, 239038.56723207,\n",
       "        239038.57851472, 239038.59012486, 239038.60388765,\n",
       "...\n",
       "        239063.17282167, 239063.18620031, 239063.21327233,\n",
       "        239063.24132911, 239063.25597695, 239063.28737118,\n",
       "        239063.31705208, 239063.34946623, 239063.36230965,\n",
       "        239063.37846475, 239063.40637802, 239063.43604431,\n",
       "        239063.44947778, 239063.47353685, 239063.49914786,\n",
       "        239063.5226075 , 239063.53649658, 239063.54952167,\n",
       "        239063.57330487, 239063.59801076, 239063.62267743,\n",
       "        239063.63802523, 239063.65456819, 239063.66857401,\n",
       "        239063.69615831, 239063.72206004, 239063.74784908,\n",
       "        239063.76048658, 239063.77257246, 239063.79628382,\n",
       "        239063.8221716 , 239063.83468586, 239063.86012432,\n",
       "        239063.88532094, 239063.89729357, 239063.92155472,\n",
       "        239063.9492357 , 239063.9729617 , 239063.99854882,\n",
       "        239064.05499844, 239064.08159417, 239064.15322525,\n",
       "        239064.20254033, 239064.26852914, 239064.30604979,\n",
       "        239064.36489975, 239064.41275244, 239064.43809711,\n",
       "        239064.48283071, 239064.51149076, 239064.54773156,\n",
       "        239064.56801931, 239064.58979249, 239064.60728623,\n",
       "        239064.62343567, 239064.65756738, 239064.67868574,\n",
       "        239064.71340343, 239064.74167352]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size_bar</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.001054 0.001054 ... 0.6265 0.6265</div><input id='attrs-5ea22c81-1222-41fa-862f-e4d8c07d32b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ea22c81-1222-41fa-862f-e4d8c07d32b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-489e1ecd-1e2e-4c11-9c46-acac10060a13' class='xr-var-data-in' type='checkbox'><label for='data-489e1ecd-1e2e-4c11-9c46-acac10060a13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "        0.00105439, 0.00105439, 0.00105439, 0.00105439, 0.00105439,\n",
       "...\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329,\n",
       "        0.62648329, 0.62648329, 0.62648329, 0.62648329, 0.62648329]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>index_in_trajectory</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 -2 1 -1 0 -2 ... 0 -6 -2 -1 4 -6</div><input id='attrs-f3142a8f-1042-4712-80a4-426147b6a0dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f3142a8f-1042-4712-80a4-426147b6a0dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f00965a-7f16-417b-b45b-7cc04010ce91' class='xr-var-data-in' type='checkbox'><label for='data-1f00965a-7f16-417b-b45b-7cc04010ce91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0, -2,  1, -1,  0, -2,  0, -1,  1, -1,  1,  0,  1, -1, -1,  1,\n",
       "         1,  0, -1, -1, -1,  1, -1, -1,  0,  1,  0, -1,  1,  1,  1,  0,\n",
       "         1, -1, -1,  1,  1,  1, -1, -1, -1, -1,  0, -1, -1, -1, -2, -1,\n",
       "        -1, -2, -1,  1,  0, -1,  1, -1, -1, -2,  1,  0,  2, -1,  0,  0,\n",
       "         1, -1,  0, -1, -1,  1,  1,  0,  0,  1, -1,  0,  1,  1,  0,  1,\n",
       "        -1, -1, -1,  0,  0,  1,  0,  0,  1, -1, -1,  0,  0,  1,  2, -1,\n",
       "         0,  1, -1, -1, -1,  1,  1,  1, -1, -1,  1,  1, -1,  1,  0,  0,\n",
       "         0,  1, -1,  0,  0,  0,  1,  2, -1, -1,  0,  0,  0,  2,  0,  0,\n",
       "         1, -1,  1,  1,  1,  1,  1,  0,  1,  0,  0,  0,  0,  0, -1,  1,\n",
       "         0, -1,  0, -1,  0, -1,  0,  0,  1, -1, -1,  0,  0,  0,  0,  2,\n",
       "         0,  1,  0,  0,  0,  1,  0,  0, -1,  1, -1, -1, -1,  0, -1,  1,\n",
       "        -1, -1, -1,  1,  0,  1,  1,  0,  1,  1,  0, -1,  0,  1,  0, -1,\n",
       "         0,  1,  0, -1,  0,  1,  1,  0, -1,  0,  0,  1,  0,  1,  1,  0,\n",
       "         0,  1, -1, -1, -1,  1,  0,  0,  0,  0,  0,  0, -1,  0,  0,  0,\n",
       "         0, -1,  0,  0,  1, -1, -1,  0, -1,  0,  0, -1,  0,  0,  1, -1,\n",
       "         0,  0,  0,  1,  0, -1,  0, -1,  0,  0,  0, -1, -1, -1,  0,  0,\n",
       "         0, -1,  0,  1,  0,  0, -1,  1,  0,  0,  0,  0,  0,  0,  1,  0,\n",
       "         1,  0,  0,  1, -1,  0,  0,  0,  0,  1,  1,  0,  0,  1,  0,  0,\n",
       "        -1,  1,  1,  1,  0,  0,  1, -1,  0,  0,  0,  0, -1,  0,  0,  1,\n",
       "         0,  0, -1,  0,  1,  0, -1,  0,  1,  1,  0,  0,  0,  0,  0,  0,\n",
       "...\n",
       "        -3,  4, -2,  2,  2,  3,  2,  1,  2, -1, -2, -2, -2,  2,  2,  1,\n",
       "         0, -2, -2,  2, -3, -2, -2, -2, -2,  2, -1,  4, -2, -7,  2,  4,\n",
       "        -2,  2, -1,  0,  2, -2,  2, -1,  5, -2,  5,  3, -1, -2, -4, -3,\n",
       "         2,  2,  5, -6, -2, -4,  5,  2,  2, -2,  2,  0, -2, -2,  1,  1,\n",
       "         3, -3, -2,  0, -5, -2,  1, -6,  2,  2,  2, -3,  2, -2,  3, -3,\n",
       "         5, -2, -3, -2, -6,  2,  1,  2,  5, -3, -1, -3,  1,  3, -6,  1,\n",
       "         2,  7, -2,  0, -3,  2, -3, -1, -4, -2, -2,  2, -3, -2, -1, -3,\n",
       "        -1,  2,  4, -1,  2, -3, -3,  4,  0, -1,  0,  3, -2,  1,  2,  0,\n",
       "        -3, -2,  1, -1,  2, -1,  2,  1,  1, -3, -2, -3,  2,  2, -5,  2,\n",
       "        -3, -4,  2, -6,  3,  2,  1,  2,  2, -5,  0,  3,  1, -1, -3,  3,\n",
       "         2,  2, -5,  2,  2,  2,  3,  2, -2, -2,  1,  6,  2, -2,  2,  3,\n",
       "         4, -2,  3, -1,  2, -2, -1, -2, -4,  2,  1,  2, -7,  5, -4,  1,\n",
       "         6,  2,  1, -1, -1,  2, -1, -1, -6, -1,  4, -5, -6, -2,  0,  6,\n",
       "        -3,  0,  1, -3, -1,  4,  3, -2, -2,  3, -1, -2, -6,  3, -1, -2,\n",
       "         3, -1, -2, -3,  0,  1,  6, -4, -7,  5, -2, -3,  2, -5,  7, -7,\n",
       "         2, -1, -7,  4,  5,  4,  2,  2, -2,  2, -2,  1, -1,  6,  3,  5,\n",
       "         2, -2,  3, -2, -2, -4, -4,  2, -1, -3,  2, -3,  6,  1,  3, -1,\n",
       "         3,  4, -2, -2, -2,  3, -5, -3,  5,  2, -2,  1,  2, -1,  1, -4,\n",
       "        -2, -5, -4, -2, -2,  2,  1, -3,  2,  2,  6,  1,  2, -2,  0, -6,\n",
       "        -2, -1,  4, -6]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7339 0.3694 ... 0.9166 0.8733</div><input id='attrs-1c8f7140-ec78-4bbe-8636-4b8deb8ef012' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c8f7140-ec78-4bbe-8636-4b8deb8ef012' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a52af78-3b43-4f01-b45e-0d85443f14fe' class='xr-var-data-in' type='checkbox'><label for='data-2a52af78-3b43-4f01-b45e-0d85443f14fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[7.33943360e-01, 3.69402267e-01, 7.31015878e-01, 6.92254875e-02,\n",
       "        7.32393213e-01, 5.79325465e-01, 7.24340296e-01, 5.66614645e-01,\n",
       "        5.69365534e-01, 5.64218022e-01, 4.46674570e-02, 7.37092082e-01,\n",
       "        7.18419770e-01, 1.32866968e-01, 1.34498389e-01, 2.86546499e-01,\n",
       "        2.84846355e-01, 5.53005949e-01, 7.03008386e-01, 5.58675434e-01,\n",
       "        5.59025969e-01, 5.38731433e-01, 2.79503024e-01, 7.10859900e-01,\n",
       "        1.30252168e-01, 2.74099828e-01, 7.04672854e-01, 7.37876197e-01,\n",
       "        2.72063114e-01, 7.09504177e-01, 5.37545438e-01, 5.27001115e-01,\n",
       "        7.05286445e-01, 7.07298689e-01, 6.94574684e-01, 2.61770054e-01,\n",
       "        7.06619411e-01, 6.70593736e-01, 5.34305933e-01, 7.01108956e-01,\n",
       "        6.75106002e-01, 5.18770004e-01, 7.18184194e-01, 5.13479782e-01,\n",
       "        7.28919672e-01, 5.07772491e-01, 5.10750923e-01, 7.35787848e-01,\n",
       "        7.03842235e-01, 1.11829076e-01, 6.40833719e-01, 4.80839984e-01,\n",
       "        2.39290979e-01, 6.82106542e-01, 6.43966214e-01, 4.83826072e-01,\n",
       "        4.91640082e-01, 4.81930980e-01, 4.62986443e-01, 6.41029417e-01,\n",
       "        4.66415911e-01, 6.29372721e-01, 4.67634299e-01, 6.24347232e-01,\n",
       "        3.76286701e-02, 4.64228253e-01, 4.58675739e-01, 3.67909586e-02,\n",
       "        4.50626683e-01, 2.19700673e-01, 3.69946575e-02, 6.03384531e-01,\n",
       "        6.19381403e-01, 6.31004792e-01, 4.40079934e-01, 4.43605878e-01,\n",
       "        2.08838780e-01, 4.67848833e-02, 5.98306022e-01, 2.38267051e-01,\n",
       "...\n",
       "        7.60876800e-01, 9.72456405e-01, 6.32110735e-01, 9.22303081e-01,\n",
       "        7.78004675e-01, 9.59974016e-01, 5.85086273e-01, 9.09992046e-01,\n",
       "        6.84359430e-01, 9.79428145e-01, 9.62880569e-01, 8.14738042e-01,\n",
       "        9.46696152e-01, 3.58481711e-01, 1.00000000e+00, 7.29341927e-01,\n",
       "        9.81871132e-01, 9.16103140e-01, 9.40182060e-01, 7.60930325e-01,\n",
       "        9.01528476e-01, 9.61634026e-01, 9.56970104e-01, 7.93498743e-01,\n",
       "        4.89375185e-01, 1.00000000e+00, 9.08528241e-01, 8.99134551e-01,\n",
       "        4.95535483e-01, 9.03560799e-01, 9.15917727e-01, 7.91614449e-01,\n",
       "        2.51914476e-01, 9.78989785e-01, 9.68532914e-01, 9.29163931e-01,\n",
       "        8.20469017e-01, 8.18025735e-01, 4.59498597e-01, 9.52950317e-01,\n",
       "        9.92828000e-01, 4.11503923e-01, 8.99917086e-01, 1.00000000e+00,\n",
       "        9.48811505e-01, 9.62910475e-01, 8.16662999e-01, 7.67570008e-01,\n",
       "        7.85991290e-01, 9.44342255e-01, 9.91707629e-01, 6.80416863e-01,\n",
       "        8.81808112e-01, 3.62135924e-01, 1.00000000e+00, 9.15395561e-01,\n",
       "        9.54356444e-01, 2.74972855e-01, 9.53290439e-01, 9.71064176e-01,\n",
       "        9.39101605e-01, 9.70880521e-01, 9.88075102e-01, 2.49084776e-01,\n",
       "        9.54406463e-01, 9.68135876e-01, 9.53299266e-01, 9.83001105e-01,\n",
       "        9.44321559e-01, 6.09050388e-01, 7.27077774e-01, 9.92009835e-01,\n",
       "        2.89562493e-01, 8.67479613e-01, 1.72176378e-02, 9.85386028e-01,\n",
       "        1.00000000e+00, 8.94169258e-01, 9.16579782e-01, 8.73288619e-01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.203 0.3133 ... 0.2325 -0.3685</div><input id='attrs-e8bc9421-a1b6-489d-8e8e-09273e566785' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e8bc9421-a1b6-489d-8e8e-09273e566785' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-867f7431-2035-452b-abdc-89772f5bc3bd' class='xr-var-data-in' type='checkbox'><label for='data-867f7431-2035-452b-abdc-89772f5bc3bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.00000000e+00,  1.20299543e+00,  3.13320098e-01,\n",
       "         2.54316560e-01,  0.00000000e+00,  1.22153349e+00,\n",
       "         0.00000000e+00,  3.53856149e-01,  3.57382898e-01,\n",
       "         3.37089980e-01,  3.60825316e-01,  0.00000000e+00,\n",
       "         3.30701243e-01,  3.22214282e-01,  3.42788863e-01,\n",
       "         3.41189631e-01,  3.62124544e-01,  0.00000000e+00,\n",
       "         3.52386459e-01,  3.29894914e-01,  3.78753724e-01,\n",
       "         3.69143447e-01,  3.28993697e-01,  3.41279915e-01,\n",
       "         0.00000000e+00,  3.90288777e-01,  0.00000000e+00,\n",
       "         3.03979224e-01,  4.07106072e-01,  3.43188896e-01,\n",
       "         3.90446429e-01,  0.00000000e+00,  3.49151253e-01,\n",
       "         3.46302228e-01,  3.64455586e-01,  3.68158545e-01,\n",
       "         3.47263073e-01,  3.99591785e-01,  3.48744785e-01,\n",
       "         3.55091975e-01,  3.92885561e-01,  3.94709826e-01,\n",
       "         0.00000000e+00,  4.11061428e-01,  3.16191742e-01,\n",
       "         4.11799519e-01,  1.60582450e+00,  3.06813452e-01,\n",
       "         3.51201046e-01,  1.68719784e+00,  4.44985264e-01,\n",
       "         4.52504206e-01,  0.00000000e+00,  3.82569414e-01,\n",
       "         4.40109018e-01,  4.65247809e-01,  4.42354553e-01,\n",
       "         1.79707529e+00,  4.95688716e-01,  0.00000000e+00,\n",
       "...\n",
       "        -1.77888486e-01,  8.07078739e-03,  4.47982898e-01,\n",
       "         1.09176424e+00, -2.11541156e-01, -1.06678837e+00,\n",
       "         2.13955133e-01, -1.12744515e-01, -1.28839333e-01,\n",
       "        -1.99757597e-01,  1.32721050e-01,  4.47079157e-01,\n",
       "        -3.31662648e-02,  7.65412930e-02,  2.67872152e-01,\n",
       "        -5.95435066e-01, -1.79955452e-01,  2.26485833e-01,\n",
       "        -2.43807733e-01, -1.45716600e-01,  2.17136995e+00,\n",
       "        -2.03682500e+00, -2.08837546e-01,  7.44701313e-02,\n",
       "         5.35792693e-02, -3.49952945e-01,  2.79048960e-01,\n",
       "        -3.50757476e-01,  7.05987672e-02, -5.88931089e-02,\n",
       "        -1.13445292e-01,  1.99714572e-01,  5.18063908e-01,\n",
       "        -4.16065012e-01, -1.42688374e-01, -2.45120677e-02,\n",
       "         3.81999767e+00, -3.51522856e+00,  2.29021800e-02,\n",
       "        -2.75086345e-02,  9.24183498e-03, -2.92359455e-01,\n",
       "         2.59677355e+00, -1.70157592e-01, -2.19369000e-02,\n",
       "        -1.47006973e+00, -2.66445379e-02, -6.13403830e-02,\n",
       "        -2.33443314e-02,  3.59812259e-01, -2.57653520e-01,\n",
       "         6.96499883e-01,  1.53830594e-01,  0.00000000e+00,\n",
       "         1.02493171e-02, -5.15596924e-01,  1.38977234e-01,\n",
       "         2.32465961e-01, -3.68539647e-01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>largest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-e36d1cfa-bc35-4c23-bf86-aea72bc5d183' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e36d1cfa-bc35-4c23-bf86-aea72bc5d183' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c29ea47-5d16-479a-b13e-485fc1e1ca73' class='xr-var-data-in' type='checkbox'><label for='data-4c29ea47-5d16-479a-b13e-485fc1e1ca73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "...\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.005175 0.01169 ... 0.02508</div><input id='attrs-7241816d-1228-41bb-96c4-4e4505e1410d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7241816d-1228-41bb-96c4-4e4505e1410d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc44c1fd-bf11-487c-ba7c-8e5428e127c5' class='xr-var-data-in' type='checkbox'><label for='data-dc44c1fd-bf11-487c-ba7c-8e5428e127c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00517459, 0.01169496, 0.00604031, 0.08761824, 0.00503338,\n",
       "        0.01172759, 0.00538369, 0.0166445 , 0.01479537, 0.01301883,\n",
       "        0.08326986, 0.00781796, 0.00919585, 0.05376162, 0.05721732,\n",
       "        0.02572934, 0.02356032, 0.01308187, 0.0055084 , 0.0112929 ,\n",
       "        0.01014582, 0.0104877 , 0.02249124, 0.00553941, 0.0481158 ,\n",
       "        0.02148463, 0.0070557 , 0.00528564, 0.02213198, 0.00529532,\n",
       "        0.01122564, 0.01054183, 0.00655872, 0.00530924, 0.0052308 ,\n",
       "        0.02113644, 0.00530203, 0.00532831, 0.01096187, 0.00796911,\n",
       "        0.00792531, 0.01584602, 0.00759428, 0.01476748, 0.0070851 ,\n",
       "        0.01394435, 0.01344566, 0.00792466, 0.00655568, 0.06078587,\n",
       "        0.00623304, 0.01256118, 0.02262918, 0.00583251, 0.00614323,\n",
       "        0.01095387, 0.01084084, 0.01113803, 0.01318924, 0.0060841 ,\n",
       "        0.01061019, 0.00530711, 0.01133107, 0.00508147, 0.06195014,\n",
       "        0.01246265, 0.01512563, 0.08630027, 0.01758984, 0.0292851 ,\n",
       "        0.067301  , 0.00658432, 0.00549861, 0.00538534, 0.0110469 ,\n",
       "        0.0119938 , 0.02155542, 0.09728824, 0.0061083 , 0.01565172,\n",
       "        0.00943018, 0.02726206, 0.01653853, 0.01511573, 0.00547202,\n",
       "        0.01516105, 0.01611536, 0.11205117, 0.09731084, 0.00553011,\n",
       "        0.06162709, 0.06951443, 0.02633684, 0.04305245, 0.02465944,\n",
       "        0.00554903, 0.00639689, 0.01049268, 0.00528421, 0.00560024,\n",
       "...\n",
       "        0.01156437, 0.02553845, 0.00653032, 0.01179196, 0.01181631,\n",
       "        0.02374745, 0.01221424, 0.01162649, 0.01257075, 0.02619406,\n",
       "        0.02579719, 0.01313506, 0.0251814 , 0.0126932 , 0.01209049,\n",
       "        0.0234226 , 0.0247512 , 0.02349892, 0.01181345, 0.01223038,\n",
       "        0.00584706, 0.02689989, 0.02421107, 0.02637739, 0.02427905,\n",
       "        0.02492956, 0.01215997, 0.02304544, 0.02370414, 0.02420901,\n",
       "        0.02579311, 0.02572862, 0.02643161, 0.01181945, 0.02654115,\n",
       "        0.02284812, 0.02363763, 0.02407133, 0.02330817, 0.02303874,\n",
       "        0.02449892, 0.0128596 , 0.02656691, 0.02740214, 0.01416684,\n",
       "        0.03079713, 0.02897781, 0.03172258, 0.01233419, 0.01558938,\n",
       "        0.02737487, 0.02914791, 0.01291599, 0.02346037, 0.02511672,\n",
       "        0.02297755, 0.01310482, 0.0125696 , 0.02332321, 0.02419833,\n",
       "        0.02419064, 0.01481237, 0.01592231, 0.01347944, 0.0269848 ,\n",
       "        0.02539992, 0.02529228, 0.01216168, 0.01164622, 0.02325696,\n",
       "        0.0253988 , 0.01203362, 0.0249321 , 0.0247117 , 0.01154516,\n",
       "        0.02378235, 0.02700868, 0.02325688, 0.02511145, 0.05563856,\n",
       "        0.02587358, 0.07075279, 0.04864026, 0.06501214, 0.03667525,\n",
       "        0.05834321, 0.04711559, 0.02443204, 0.04421101, 0.02796825,\n",
       "        0.03534438, 0.01949929, 0.02069776, 0.0164009 , 0.01565217,\n",
       "        0.03363069, 0.02031855, 0.03380919, 0.02754598, 0.02508021]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.261e+05 -1.261e+05 ... 2.023e+03</div><input id='attrs-9ff42bf6-dbff-4d0e-9b8a-ee4352da7916' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ff42bf6-dbff-4d0e-9b8a-ee4352da7916' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6287ca6-cb3a-42e5-829f-7848091d61b5' class='xr-var-data-in' type='checkbox'><label for='data-a6287ca6-cb3a-42e5-829f-7848091d61b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-126110.54744933, -126108.90743193, -127307.30259279,\n",
       "        -127598.17301158, -127866.79768304, -127864.29001078,\n",
       "        -129029.76816734, -129028.47714629, -129343.45473864,\n",
       "        -129684.38301891, -130002.46121756, -130339.00903205,\n",
       "        -130339.88787803, -130642.64750867, -130940.4944551 ,\n",
       "        -131263.20623993, -131582.61406006, -131909.71612401,\n",
       "        -131908.80174485, -132217.25929024, -132528.43565793,\n",
       "        -132892.47766399, -133219.20255527, -133529.27423339,\n",
       "        -133850.78329591, -133851.09051154, -134207.0502691 ,\n",
       "        -134203.40717784, -134472.61419272, -134840.03400718,\n",
       "        -135165.51004611, -135515.30844162, -135515.48563956,\n",
       "        -135816.14368644, -136134.16009082, -136460.42983168,\n",
       "        -136793.31347032, -137109.09828971, -137433.61039573,\n",
       "        -137762.45474626, -138104.53253057, -138444.037025  ,\n",
       "        -138794.50736453, -138794.92828262, -139142.08858137,\n",
       "        -139446.73810442, -139802.48849134, -141217.28099306,\n",
       "        -141494.36173805, -141796.2870764 , -143239.51168574,\n",
       "        -143609.15156182, -143989.15882763, -143988.45124851,\n",
       "        -144342.30790041, -144696.21472148, -145099.06125443,\n",
       "        -145484.84143482, -147023.16540987, -147427.50974119,\n",
       "...\n",
       "           2018.90600752,    2017.9235055 ,    2019.315415  ,\n",
       "           2023.61505699,    2023.63258078,    2021.89376446,\n",
       "           2022.52895134,    2023.88995348,    2022.8498384 ,\n",
       "           2019.10370732,    2020.06000196,    2023.05788192,\n",
       "           2019.25072106,    2020.60694761,    2021.65897781,\n",
       "           2022.02684607,    2021.96693676,    2022.92038921,\n",
       "           2021.16292153,    2019.57176344,    2024.98152973,\n",
       "           2023.24080994,    2018.39749305,    2019.4108832 ,\n",
       "           2019.44204182,    2019.31667143,    2020.00490413,\n",
       "           2020.3351654 ,    2018.18176216,    2018.86229554,\n",
       "           2021.36908188,    2018.21949523,    2022.49245543,\n",
       "           2019.53615528,    2019.60256239,    2017.75537244,\n",
       "           2025.46859447,    2023.58940904,    2019.07369736,\n",
       "           2020.19091089,    2020.76747319,    2020.50526594,\n",
       "           2024.94936792,    2023.64171183,    2021.28726845,\n",
       "           2020.09248169,    2018.02965684,    2018.10328861,\n",
       "           2019.12601849,    2020.27103278,    2018.11713223,\n",
       "           2022.30286566,    2023.49297093,    2025.75763808,\n",
       "           2025.86965923,    2023.75596278,    2020.99810468,\n",
       "           2021.7530354 ,    2022.7638606 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>process_time_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.005175 0.01156 ... 0.02481</div><input id='attrs-a7d78ece-c487-4175-9fe8-a402ae707485' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7d78ece-c487-4175-9fe8-a402ae707485' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b67fd423-4c84-4e76-b4d8-aa5fbbe58bc9' class='xr-var-data-in' type='checkbox'><label for='data-b67fd423-4c84-4e76-b4d8-aa5fbbe58bc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.005175, 0.011564, 0.006043, 0.087015, 0.005035, 0.011573,\n",
       "        0.005385, 0.01614 , 0.01445 , 0.01268 , 0.081182, 0.007744,\n",
       "        0.008613, 0.052245, 0.055647, 0.025255, 0.023196, 0.012781,\n",
       "        0.005496, 0.011213, 0.010148, 0.010441, 0.02223 , 0.005474,\n",
       "        0.04718 , 0.021332, 0.006901, 0.005287, 0.021981, 0.005251,\n",
       "        0.011064, 0.010543, 0.006538, 0.005311, 0.005233, 0.021059,\n",
       "        0.005278, 0.00531 , 0.010964, 0.007815, 0.007765, 0.015359,\n",
       "        0.007281, 0.014314, 0.006961, 0.01373 , 0.013215, 0.007749,\n",
       "        0.006442, 0.059053, 0.006115, 0.012148, 0.022452, 0.005687,\n",
       "        0.006066, 0.010834, 0.01079 , 0.011066, 0.013063, 0.005843,\n",
       "        0.010568, 0.005308, 0.011227, 0.005083, 0.060798, 0.01229 ,\n",
       "        0.014788, 0.083035, 0.01697 , 0.028384, 0.066152, 0.006442,\n",
       "        0.005477, 0.005368, 0.010902, 0.011908, 0.02149 , 0.094879,\n",
       "        0.00597 , 0.015268, 0.008768, 0.026631, 0.016094, 0.014469,\n",
       "        0.005447, 0.014698, 0.015783, 0.108906, 0.095779, 0.005521,\n",
       "        0.058969, 0.06786 , 0.02533 , 0.042856, 0.024381, 0.005548,\n",
       "        0.006252, 0.010476, 0.005286, 0.005543, 0.020679, 0.048535,\n",
       "        0.01117 , 0.005785, 0.025839, 0.011492, 0.025712, 0.014704,\n",
       "        0.006694, 0.012247, 0.00576 , 0.005745, 0.007692, 0.012556,\n",
       "        0.029061, 0.008315, 0.00713 , 0.059914, 0.011016, 0.049148,\n",
       "...\n",
       "        0.023829, 0.011639, 0.023514, 0.024691, 0.011678, 0.011767,\n",
       "        0.023462, 0.012718, 0.02461 , 0.006702, 0.048304, 0.023169,\n",
       "        0.023805, 0.023351, 0.023707, 0.023264, 0.011557, 0.02537 ,\n",
       "        0.006491, 0.011794, 0.011743, 0.023679, 0.012108, 0.011627,\n",
       "        0.012506, 0.025833, 0.025422, 0.013043, 0.024956, 0.012581,\n",
       "        0.012029, 0.023411, 0.024637, 0.023493, 0.0118  , 0.012138,\n",
       "        0.005848, 0.026553, 0.024136, 0.026114, 0.02423 , 0.024768,\n",
       "        0.012047, 0.023024, 0.023575, 0.024065, 0.025669, 0.025468,\n",
       "        0.02634 , 0.011799, 0.026048, 0.02285 , 0.023589, 0.023961,\n",
       "        0.02329 , 0.023039, 0.024345, 0.012747, 0.026385, 0.026956,\n",
       "        0.013771, 0.030114, 0.028286, 0.030752, 0.012322, 0.015018,\n",
       "        0.026633, 0.028615, 0.012817, 0.023422, 0.025009, 0.022961,\n",
       "        0.013044, 0.01238 , 0.023304, 0.024106, 0.024075, 0.014595,\n",
       "        0.014761, 0.013298, 0.026664, 0.025162, 0.025088, 0.012138,\n",
       "        0.011647, 0.023258, 0.025348, 0.012035, 0.024734, 0.024568,\n",
       "        0.011546, 0.023711, 0.026802, 0.023194, 0.024793, 0.041261,\n",
       "        0.020572, 0.043167, 0.035857, 0.038676, 0.02695 , 0.03385 ,\n",
       "        0.036753, 0.020922, 0.028936, 0.025478, 0.034091, 0.017386,\n",
       "        0.019361, 0.015611, 0.014915, 0.032012, 0.018702, 0.031671,\n",
       "        0.026757, 0.024809]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3093 2.841 ... 0.2366 0.6378</div><input id='attrs-ccc12c6d-f445-40b7-b7e9-1dd8a8f7a8d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ccc12c6d-f445-40b7-b7e9-1dd8a8f7a8d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9be59f1-379e-482a-921e-e8fe448f914e' class='xr-var-data-in' type='checkbox'><label for='data-a9be59f1-379e-482a-921e-e8fe448f914e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 3.09323420e-01,  2.84083798e+00,  3.13320098e-01,\n",
       "         2.00095798e+02,  3.11437732e-01,  1.22153349e+00,\n",
       "         3.22493975e-01,  1.31704209e+00,  1.26672900e+00,\n",
       "         1.32230265e+00,  1.25246333e+03,  3.05042453e-01,\n",
       "         3.30701243e-01,  2.65001568e+01,  2.69180507e+01,\n",
       "         5.78749833e+00,  5.80167932e+00,  1.36869802e+00,\n",
       "         3.52386459e-01,  1.38599023e+00,  1.33730422e+00,\n",
       "         1.44414940e+00,  5.90429240e+00,  3.41279915e-01,\n",
       "         2.82966580e+01,  5.96938221e+00,  3.50021621e-01,\n",
       "         3.03979224e-01,  5.99879081e+00,  3.43188896e-01,\n",
       "         1.47168477e+00,  1.52719651e+00,  3.49151253e-01,\n",
       "         3.46302228e-01,  3.64455586e-01,  6.33968044e+00,\n",
       "         3.47263073e-01,  3.99591785e-01,  1.48492803e+00,\n",
       "         3.55091975e-01,  3.92885561e-01,  1.61887518e+00,\n",
       "         3.31029205e-01,  1.61498045e+00,  3.16191742e-01,\n",
       "         1.64338074e+00,  1.60582450e+00,  3.06813452e-01,\n",
       "         3.51201046e-01,  3.34886042e+01,  4.44985264e-01,\n",
       "         1.76524749e+00,  7.33298408e+00,  3.82569414e-01,\n",
       "         4.40109018e-01,  1.83076022e+00,  1.80240754e+00,\n",
       "         1.79707529e+00,  1.90878551e+00,  4.44679931e-01,\n",
       "...\n",
       "        -1.77888486e-01,  1.22102703e-01,  4.47982898e-01,\n",
       "         1.09176424e+00, -1.39097436e+00, -1.06678837e+00,\n",
       "         3.10162481e-01,  1.61724602e+00,  2.19425602e-01,\n",
       "        -3.88543713e-01,  4.34221260e-01,  3.18546961e+00,\n",
       "        -7.62623365e-01, -5.97326888e-01, -5.95494055e-01,\n",
       "        -5.95435066e-01,  7.40057015e-01,  3.04672932e+00,\n",
       "         2.62040730e-01, -2.10626789e-01,  2.17136995e+00,\n",
       "        -2.07000219e+00, -2.83928874e-01, -1.89735742e-01,\n",
       "        -2.02326707e-01,  5.92513155e-01,  3.03735788e-01,\n",
       "         3.96924953e-01,  1.49697295e-01, -1.75904532e-01,\n",
       "         7.54583544e-01,  2.59751777e-01,  1.81576474e+00,\n",
       "        -4.16065012e-01,  2.73332846e-01,  1.08988588e-01,\n",
       "         4.39229285e+00, -3.93285388e+00, -2.17049250e-01,\n",
       "        -2.55580166e-01, -1.19470078e-01, -2.92359455e-01,\n",
       "         2.80149100e+00, -8.15152988e-01, -1.57043121e+00,\n",
       "        -1.47006973e+00, -1.05007441e-01,  1.33410874e-01,\n",
       "         1.17422145e+00,  6.28230442e-01, -2.57653520e-01,\n",
       "         1.90293133e+00, -4.87670138e-01,  5.71771709e+00,\n",
       "        -6.53090682e-01, -5.15596924e-01,  2.63491896e-01,\n",
       "         2.36628588e-01,  6.37804554e-01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 1 5 1 2 1 2 ... 2 2 2 3 2 3 3 3</div><input id='attrs-818aca22-2c6f-4a1e-891d-c33e6a396e86' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-818aca22-2c6f-4a1e-891d-c33e6a396e86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-100b1553-0a97-4a59-8c22-3629235c0d32' class='xr-var-data-in' type='checkbox'><label for='data-100b1553-0a97-4a59-8c22-3629235c0d32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1, 2, 1, 5, 1, 2, 1, 2, 2, 2, 5, 1, 1, 4, 4, 3, 3, 2, 1, 2, 2, 2,\n",
       "        3, 1, 4, 3, 1, 1, 3, 1, 2, 2, 1, 1, 1, 3, 1, 1, 2, 1, 1, 2, 1, 2,\n",
       "        1, 2, 2, 1, 1, 4, 1, 2, 3, 1, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 5, 2,\n",
       "        2, 5, 2, 3, 5, 1, 1, 1, 2, 2, 3, 5, 1, 2, 1, 3, 2, 2, 1, 2, 2, 5,\n",
       "        5, 1, 4, 5, 3, 4, 3, 1, 1, 2, 1, 1, 3, 4, 2, 1, 3, 2, 3, 2, 1, 2,\n",
       "        1, 1, 1, 2, 3, 1, 1, 4, 2, 4, 5, 2, 1, 1, 5, 3, 2, 1, 2, 2, 1, 1,\n",
       "        2, 2, 3, 1, 1, 1, 1, 2, 5, 2, 1, 1, 1, 3, 1, 3, 1, 4, 1, 2, 1, 1,\n",
       "        1, 2, 4, 1, 1, 2, 5, 1, 1, 3, 1, 3, 1, 1, 2, 2, 1, 4, 2, 1, 2, 1,\n",
       "        5, 5, 3, 2, 3, 4, 1, 1, 2, 5, 1, 1, 1, 4, 1, 3, 1, 2, 3, 3, 1, 1,\n",
       "        2, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 4, 2, 2, 1, 4, 4, 1, 1, 1, 1, 1,\n",
       "        2, 1, 3, 2, 2, 1, 2, 1, 1, 2, 1, 3, 1, 4, 4, 1, 3, 1, 1, 1, 2, 3,\n",
       "        2, 4, 1, 2, 4, 1, 2, 1, 1, 1, 1, 1, 1, 4, 1, 2, 3, 2, 1, 1, 2, 4,\n",
       "        1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 3, 1, 2, 3, 4, 1, 4, 4, 3, 1, 4, 2,\n",
       "        2, 2, 3, 1, 2, 2, 1, 1, 3, 2, 2, 1, 1, 1, 3, 2, 4, 1, 1, 1, 2, 1,\n",
       "        2, 2, 2, 1, 1, 3, 1, 2, 2, 2, 1, 3, 2, 2, 2, 4, 2, 2, 2, 1, 1, 1,\n",
       "        2, 4, 4, 1, 1, 1, 1, 4, 1, 2, 2, 2, 1, 4, 1, 1, 1, 1, 1, 2, 4, 4,\n",
       "        3, 3, 2, 3, 1, 4, 1, 3, 1, 1, 1, 1, 3, 1, 1, 1, 3, 2, 4, 1, 1, 1,\n",
       "        2, 2, 1, 1, 1, 4, 1, 1, 1, 4, 3, 1, 1, 3, 1, 2, 1, 2, 2, 2, 2, 2,\n",
       "        3, 2, 1, 4, 4, 2, 2, 1, 4, 2, 1, 2, 3, 2, 1, 3, 1, 3, 1, 1, 2, 1,\n",
       "        4, 3, 1, 1, 2, 1, 2, 2, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1,\n",
       "...\n",
       "        2, 3, 3, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 3, 4, 2, 2, 3, 2, 3, 3,\n",
       "        3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 2, 4, 2, 2, 3, 3,\n",
       "        2, 2, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 2, 3, 2, 2,\n",
       "        2, 3, 2, 2, 3, 2, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 2, 2, 3, 3, 3, 3,\n",
       "        3, 2, 2, 3, 3, 3, 2, 3, 2, 2, 2, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3,\n",
       "        2, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3,\n",
       "        2, 2, 3, 3, 3, 2, 3, 3, 2, 2, 1, 3, 3, 2, 3, 3, 3, 2, 2, 2, 2, 3,\n",
       "        2, 3, 3, 4, 2, 2, 2, 3, 2, 3, 2, 3, 3, 3, 3, 2, 3, 2, 3, 3, 3, 3,\n",
       "        3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 2, 2, 2, 3, 3, 3, 2, 3, 4, 2, 3,\n",
       "        3, 3, 2, 2, 3, 3, 3, 2, 3, 2, 2, 2, 3, 3, 2, 4, 3, 3, 3, 2, 2, 3,\n",
       "        3, 2, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "        3, 3, 3, 3, 1, 3, 2, 2, 3, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3,\n",
       "        3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2,\n",
       "        2, 2, 3, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 2, 2, 3, 3, 3, 2, 3, 2, 3,\n",
       "        2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 2, 3, 2, 3, 1, 4, 3,\n",
       "        3, 3, 3, 3, 2, 3, 1, 2, 2, 3, 2, 2, 2, 3, 3, 2, 3, 2, 2, 3, 3, 3,\n",
       "        2, 2, 1, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3,\n",
       "        3, 2, 3, 3, 2, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 3, 2, 2, 3, 3, 3, 2,\n",
       "        2, 2, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3, 3,\n",
       "        3, 3, 3, 2, 3, 3, 3, 2, 2, 2, 2, 3, 2, 3, 3, 3]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reached_max_treedepth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-0953303b-7e2a-4f97-a565-48f338130bc3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0953303b-7e2a-4f97-a565-48f338130bc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e6eb758-ee38-490a-98f0-3bda18f3953b' class='xr-var-data-in' type='checkbox'><label for='data-4e6eb758-ee38-490a-98f0-3bda18f3953b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "...\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0008649 0.0008649 ... 0.6154</div><input id='attrs-3973cc97-bf3c-4911-bb7d-1f6cc858ab0b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3973cc97-bf3c-4911-bb7d-1f6cc858ab0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-362ad867-6d8b-4186-b219-4892ee97fe05' class='xr-var-data-in' type='checkbox'><label for='data-362ad867-6d8b-4186-b219-4892ee97fe05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "        0.00086487, 0.00086487, 0.00086487, 0.00086487, 0.00086487,\n",
       "...\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ,\n",
       "        0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 , 0.6154398 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.261e+05 1.273e+05 ... -2.019e+03</div><input id='attrs-52469d16-5b0d-4d4a-aee9-89210bc15c04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52469d16-5b0d-4d4a-aee9-89210bc15c04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6639759e-5ef4-456a-9b1e-b90d4de81fb0' class='xr-var-data-in' type='checkbox'><label for='data-6639759e-5ef4-456a-9b1e-b90d4de81fb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[126111.97304104, 127308.38973173, 127602.01426271,\n",
       "        127868.17301158, 127868.17301158, 129030.29635843,\n",
       "        129030.29635843, 129346.13074126, 129685.50982286,\n",
       "        130005.84682506, 130340.59900443, 130340.59900443,\n",
       "        130644.51065025, 130941.84160047, 131267.11307693,\n",
       "        131587.21805024, 131910.52262939, 131910.52262939,\n",
       "        132220.66182175, 132534.77430488, 132894.96895261,\n",
       "        133221.68344402, 133530.67795811, 133852.92349486,\n",
       "        133852.92349486, 134208.62322638, 134208.62322638,\n",
       "        134476.1560792 , 134843.23369956, 135166.95020591,\n",
       "        135517.0739194 , 135517.0739194 , 135817.57566641,\n",
       "        136135.73584953, 136466.42946949, 136798.19188612,\n",
       "        137110.62774034, 137435.73204825, 137763.88068992,\n",
       "        138105.69485857, 138445.13436773, 138797.18750717,\n",
       "        138797.18750717, 139145.70968521, 139448.09273176,\n",
       "        139805.10257586, 141223.66451673, 141498.31837709,\n",
       "        141801.93116554, 143240.20602172, 143611.13186396,\n",
       "        143990.73579034, 143990.73579034, 144343.81831394,\n",
       "        144701.21387575, 145100.92333964, 145486.81455033,\n",
       "        147027.45691333, 147428.1241501 , 147428.1241501 ,\n",
       "...\n",
       "         -2017.32141947,  -2017.43518709,  -2018.10242722,\n",
       "         -2021.98017502,  -2021.87720623,  -2018.9915373 ,\n",
       "         -2020.75406819,  -2021.57128082,  -2017.84778419,\n",
       "         -2017.9109664 ,  -2018.87607538,  -2018.80771121,\n",
       "         -2018.60444721,  -2019.79741884,  -2020.57598383,\n",
       "         -2017.85095897,  -2019.15511673,  -2019.19458488,\n",
       "         -2018.83620606,  -2018.83926307,  -2024.16473528,\n",
       "         -2018.33396103,  -2017.55171041,  -2018.494725  ,\n",
       "         -2018.57631404,  -2017.27565151,  -2019.54533197,\n",
       "         -2017.18959083,  -2017.57896644,  -2017.90496436,\n",
       "         -2017.03866949,  -2017.79525953,  -2019.6057958 ,\n",
       "         -2017.28611594,  -2017.39849206,  -2017.20509389,\n",
       "         -2025.0465276 ,  -2018.23549144,  -2018.12588069,\n",
       "         -2018.78507502,  -2019.91851512,  -2017.5484166 ,\n",
       "         -2021.85437766,  -2020.51225268,  -2020.30512981,\n",
       "         -2017.32179769,  -2017.67706279,  -2017.40170612,\n",
       "         -2017.26741597,  -2017.90916964,  -2017.15981541,\n",
       "         -2019.86564509,  -2022.4352008 ,  -2022.4352008 ,\n",
       "         -2023.68011455,  -2018.78827469,  -2018.58104932,\n",
       "         -2020.26741859,  -2019.22581519]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fa6f61a1-c22f-4c03-a0c6-32c6105419c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa6f61a1-c22f-4c03-a0c6-32c6105419c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eea31cc8-d51c-4b05-9768-14ca8f9b19e8' class='xr-var-data-in' type='checkbox'><label for='data-eea31cc8-d51c-4b05-9768-14ca8f9b19e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, False, False, False, False, False, False,\n",
       "        False,  True, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False,  True, False, False,  True, False, False,  True, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False,  True, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False,  True, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False,  True, False, False, False,\n",
       "...\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False, False, False, False, False,\n",
       "        False, False, False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 3.0 1.0 31.0 ... 7.0 7.0 7.0</div><input id='attrs-23ba0303-4e46-4a6e-a337-8d11cdb555e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23ba0303-4e46-4a6e-a337-8d11cdb555e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e77e89b-cd8c-4111-89a2-e310d61e7437' class='xr-var-data-in' type='checkbox'><label for='data-8e77e89b-cd8c-4111-89a2-e310d61e7437' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 1.,  3.,  1., 31.,  1.,  3.,  1.,  3.,  3.,  3., 22.,  1.,  1.,\n",
       "        15., 15.,  7.,  7.,  3.,  1.,  3.,  3.,  3.,  7.,  1., 15.,  7.,\n",
       "         1.,  1.,  7.,  1.,  3.,  3.,  1.,  1.,  1.,  7.,  1.,  1.,  3.,\n",
       "         1.,  1.,  3.,  1.,  3.,  1.,  3.,  3.,  1.,  1., 15.,  1.,  3.,\n",
       "         7.,  1.,  1.,  3.,  3.,  3.,  3.,  1.,  3.,  1.,  3.,  1., 20.,\n",
       "         3.,  3., 20.,  3.,  7., 20.,  1.,  1.,  1.,  3.,  3.,  7., 31.,\n",
       "         1.,  3.,  1.,  7.,  3.,  3.,  1.,  3.,  3., 31., 31.,  1., 15.,\n",
       "        19.,  7., 15.,  7.,  1.,  1.,  3.,  1.,  1.,  7., 15.,  3.,  1.,\n",
       "         7.,  3.,  7.,  3.,  1.,  3.,  1.,  1.,  1.,  3.,  7.,  1.,  1.,\n",
       "        15.,  3., 15., 31.,  3.,  1.,  1., 31.,  7.,  3.,  1.,  3.,  3.,\n",
       "         1.,  1.,  3.,  3.,  7.,  1.,  1.,  1.,  1.,  3., 31.,  3.,  1.,\n",
       "         1.,  1.,  7.,  1.,  7.,  1., 15.,  1.,  3.,  1.,  1.,  1.,  3.,\n",
       "        15.,  1.,  1.,  3., 17.,  1.,  1.,  7.,  1.,  7.,  1.,  1.,  3.,\n",
       "         3.,  1., 15.,  3.,  1.,  3.,  1., 17., 31.,  7.,  3.,  7., 15.,\n",
       "         1.,  1.,  3., 31.,  1.,  1.,  1., 15.,  1.,  7.,  1.,  3.,  7.,\n",
       "         7.,  1.,  1.,  3.,  3.,  3.,  1.,  1.,  3.,  1.,  3.,  1.,  1.,\n",
       "         1., 15.,  3.,  3.,  1., 15., 15.,  1.,  1.,  1.,  1.,  1.,  3.,\n",
       "         1.,  7.,  3.,  3.,  1.,  3.,  1.,  1.,  3.,  1.,  7.,  1., 15.,\n",
       "        15.,  1.,  7.,  1.,  1.,  1.,  3.,  7.,  3., 15.,  1.,  3., 15.,\n",
       "         1.,  3.,  1.,  1.,  1.,  1.,  1.,  1., 15.,  1.,  3.,  7.,  3.,\n",
       "...\n",
       "         3.,  3.,  7.,  3.,  7.,  3.,  3.,  3.,  3.,  7.,  7.,  5.,  3.,\n",
       "         7., 15.,  3.,  7.,  7.,  7.,  3.,  3.,  7.,  7.,  7.,  3.,  7.,\n",
       "         3.,  3.,  3.,  7.,  7.,  3., 15.,  7.,  7.,  7.,  3.,  3.,  7.,\n",
       "         7.,  3.,  3.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
       "         3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
       "         1.,  7.,  3.,  3.,  7.,  3.,  7.,  7.,  3.,  3.,  3.,  3.,  3.,\n",
       "         3.,  3.,  7.,  3.,  7.,  7.,  3.,  7.,  7.,  7.,  3.,  7.,  7.,\n",
       "         7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
       "         3.,  3.,  3.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  3.,  7.,\n",
       "         7.,  3.,  3.,  7.,  7.,  7.,  3.,  7.,  3.,  7.,  3.,  7.,  7.,\n",
       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  3.,  3.,\n",
       "         7.,  3.,  7.,  1., 15.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  1.,\n",
       "         3.,  3.,  7.,  3.,  3.,  3.,  7.,  7.,  3.,  7.,  3.,  3.,  7.,\n",
       "         7.,  7.,  3.,  3.,  1.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,\n",
       "         7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
       "         7.,  7.,  3.,  7.,  7.,  7.,  3.,  3.,  7.,  7.,  3.,  7.,  7.,\n",
       "         7.,  3.,  3.,  7.,  7.,  7.,  3.,  3.,  3.,  7.,  7.,  7.,  3.,\n",
       "         3.,  7.,  7.,  3.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  5.,  7.,  3.,  3.,  3.,\n",
       "         3.,  7.,  3.,  7.,  7.,  7.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smallest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-cd69c001-08b5-4f50-a4bd-1d56d7fa848c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd69c001-08b5-4f50-a4bd-1d56d7fa848c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a80a7b95-e604-411f-abf8-ad8b494bb9ec' class='xr-var-data-in' type='checkbox'><label for='data-a80a7b95-e604-411f-abf8-ad8b494bb9ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "...\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "        nan, nan, nan, nan, nan, nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d5878e39-dc25-4046-b57c-f7a056308274' class='xr-section-summary-in' type='checkbox'  ><label for='section-d5878e39-dc25-4046-b57c-f7a056308274' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b03ef414-6a78-46d0-8562-50ecb57ed266' class='xr-index-data-in' type='checkbox'/><label for='index-b03ef414-6a78-46d0-8562-50ecb57ed266' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3a4939a1-adb0-47a3-b34b-2479e67f8de2' class='xr-index-data-in' type='checkbox'/><label for='index-3a4939a1-adb0-47a3-b34b-2479e67f8de2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       490, 491, 492, 493, 494, 495, 496, 497, 498, 499],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c384f0a-ab24-4996-aadc-5219d752013a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c384f0a-ab24-4996-aadc-5219d752013a' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-07-12T14:52:02.733467</dd><dt><span>arviz_version :</span></dt><dd>0.14.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.5.0</dd><dt><span>sampling_time :</span></dt><dd>55.89794373512268</dd><dt><span>tuning_steps :</span></dt><dd>500</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.11.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_datae1d35697-738d-4536-b970-cc4baea3eccc\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_datae1d35697-738d-4536-b970-cc4baea3eccc\" class = \"xr-section-summary\">observed_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                  (rt,response_obs: 1000, rt,response_extra_dim_0: 2)\n",
       "Coordinates:\n",
       "  * rt,response_obs          (rt,response_obs) int64 0 1 2 3 ... 996 997 998 999\n",
       "  * rt,response_extra_dim_0  (rt,response_extra_dim_0) int64 0 1\n",
       "Data variables:\n",
       "    rt,response              (rt,response_obs, rt,response_extra_dim_0) float32 ...\n",
       "Attributes:\n",
       "    created_at:                  2023-07-12T14:52:02.763090\n",
       "    arviz_version:               0.14.0\n",
       "    inference_library:           pymc\n",
       "    inference_library_version:   5.5.0\n",
       "    modeling_interface:          bambi\n",
       "    modeling_interface_version:  0.11.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-254e529e-4b04-42f2-b367-83a1ff93ebd2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-254e529e-4b04-42f2-b367-83a1ff93ebd2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>rt,response_obs</span>: 1000</li><li><span class='xr-has-index'>rt,response_extra_dim_0</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d4fc9e9a-ed22-49cb-8bd8-32422cc80079' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d4fc9e9a-ed22-49cb-8bd8-32422cc80079' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rt,response_obs</span></div><div class='xr-var-dims'>(rt,response_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-23b3d63c-5ec9-41c5-b1ad-5cb0d11daac4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-23b3d63c-5ec9-41c5-b1ad-5cb0d11daac4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94021ded-f9a6-4571-b893-cbd78952bc70' class='xr-var-data-in' type='checkbox'><label for='data-94021ded-f9a6-4571-b893-cbd78952bc70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>rt,response_extra_dim_0</span></div><div class='xr-var-dims'>(rt,response_extra_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-1afa2a82-bbeb-499a-9089-c7bce0438e7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1afa2a82-bbeb-499a-9089-c7bce0438e7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-255924f5-88a1-4c29-854e-122414a84d29' class='xr-var-data-in' type='checkbox'><label for='data-255924f5-88a1-4c29-854e-122414a84d29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b0bf86c5-1a24-402f-956b-d19d614c7b30' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b0bf86c5-1a24-402f-956b-d19d614c7b30' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>rt,response</span></div><div class='xr-var-dims'>(rt,response_obs, rt,response_extra_dim_0)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.614 1.0 5.922 ... 1.0 1.278 -1.0</div><input id='attrs-1e013632-3e19-4dd9-af64-b4f29d0bdc64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e013632-3e19-4dd9-af64-b4f29d0bdc64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b758b37f-fc6e-4022-aab1-c08345d0fffe' class='xr-var-data-in' type='checkbox'><label for='data-b758b37f-fc6e-4022-aab1-c08345d0fffe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 1.6140053,  1.       ],\n",
       "       [ 5.9217753,  1.       ],\n",
       "       [ 2.2360344,  1.       ],\n",
       "       ...,\n",
       "       [ 2.9179926,  1.       ],\n",
       "       [ 1.3109913,  1.       ],\n",
       "       [ 1.277991 , -1.       ]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-75cae315-d76d-4b45-8e36-09c0c317ad0c' class='xr-section-summary-in' type='checkbox'  ><label for='section-75cae315-d76d-4b45-8e36-09c0c317ad0c' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>rt,response_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-83dc8cda-5873-4e18-bcac-f214ad25fe25' class='xr-index-data-in' type='checkbox'/><label for='index-83dc8cda-5873-4e18-bcac-f214ad25fe25' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "       ...\n",
       "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;rt,response_obs&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>rt,response_extra_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-89e12387-1325-47ea-82e6-1cbaa6ec71ef' class='xr-index-data-in' type='checkbox'/><label for='index-89e12387-1325-47ea-82e6-1cbaa6ec71ef' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1], dtype=&#x27;int64&#x27;, name=&#x27;rt,response_extra_dim_0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f99a7e01-6892-48b4-9a22-05fc0ba02455' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f99a7e01-6892-48b4-9a22-05fc0ba02455' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-07-12T14:52:02.763090</dd><dt><span>arviz_version :</span></dt><dd>0.14.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.5.0</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.11.0</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
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       ".xr-wrap{width:700px!important;} </style>"
      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> sample_stats\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddm_model_analytical_override.sample(draws=500, tune=500, chains=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Custom Likelihoods\n",
    "\n",
    "If you are specifying a model with a kind of likelihood that's not included in the list above, then HSSM considers that you are using a custom model with custom likelihoods. In this case, you will need to specify your entire model. Below is the procedure to specify a custom model:\n",
    "\n",
    "1. Specify a `model` string. It can be any string that helps identify the model, but if it is not one of the model strings supported in the `ssm_simulators` package [see full list here](https://github.com/AlexanderFengler/ssm-simulators/blob/main/ssms/config/config.py), you will need to supply a `RandomVariable` class to `model_config` detailed below. Otherwise, you can still perform MCMC sampling, but sampling from the posterior predictive distribution will raise a ValueError.\n",
    "\n",
    "2. Specify a `model_config`. It typically contains the following fields:\n",
    "\n",
    "   - `\"list_params\"`: Required if your `model` string is not one of `ddm`, `ddm_sdv`, `angle`, `levy`, `ornstein`, `weibull`, `race_no_bias_angle_4` and `ddm_seq2_no_bias`. A list of `str` indicating the parameters of the model.\n",
    "     The order in which the parameters are specified in this list is important.\n",
    "     Values for each parameter will be passed to the likelihood function in this\n",
    "     order.\n",
    "   - `\"backend\"`: Optional. Only used when `loglik_kind` is `approx_differentiable` and\n",
    "     an onnx file is supplied for the likelihood approximation network (LAN).\n",
    "     Valid values are `\"jax\"` or `\"pytensor\"`. It determines whether the LAN in\n",
    "     ONNX should be converted to `\"jax\"` or `\"pytensor\"`. If not provided,\n",
    "     `jax` will be used for maximum performance.\n",
    "   - `\"default_priors\"`: Optional. A `dict` indicating the default priors for each parameter.\n",
    "   - `\"bounds\"`: Optional. A `dict` of `(lower, upper)` tuples indicating the acceptable boundaries for each parameter. In the case\n",
    "     of LAN, these bounds are training boundaries.\n",
    "   - `\"rv\"`: Optional. Can be a `RandomVariable` class containing the user's own\n",
    "     `rng_fn` function for sampling from the distribution that the user is\n",
    "     supplying. If not supplied, HSSM will automatically generate a\n",
    "     `RandomVariable` using the simulator identified by `model` from the\n",
    "     `ssm_simulators` package. If `model` is not supported in `ssm_simulators`,\n",
    "     a warning will be raised letting the user know that sampling from the\n",
    "     `RandomVariable` will result in errors.\n",
    "<br> </br>     \n",
    "\n",
    "3. Specify `loglik` and `loglik_kind`.\n",
    "\n",
    "4. Specify parameter priors in `include`.\n",
    "\n",
    "**NOTE**:\n",
    "\n",
    " `default_priors` and `bounds` in `model_config` specifies  **default** priors and bounds for the model. Actual priors and defaults should be provided via the `include` list and will override these defaults.\n",
    "\n",
    "Below are a few examples:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "# An angle model with an analytical likelihood function.\n",
    "# Because `model` is known, no `list_params` needs to be provided.\n",
    "\n",
    "custom_angle_model = hssm.HSSM(\n",
    "    data,\n",
    "    model=\"angle\",\n",
    "    model_config={\n",
    "        \"bounds\": {\n",
    "            \"v\": (-3.0, 3.0),\n",
    "            \"a\": (0.3, 3.0),\n",
    "            \"z\": (0.1, 0.9),\n",
    "            \"t\": (0.001, 2.0),\n",
    "            \"theta\": (-0.1, 1.3),\n",
    "        }  # bounds will be used to create Uniform (uninformative) priors by default\n",
    "        # if priors are not supplied in `include`.\n",
    "    },\n",
    "    loglik=custom_angle_logp,\n",
    "    loglik_kind=\"analytical\",\n",
    ")\n",
    "\n",
    "# A fully customized model with a custom likelihood function.\n",
    "# Because `model` is not known, a `list_params` needs to be provided.\n",
    "\n",
    "my_custom_model = hssm.HSSM(\n",
    "    data,\n",
    "    model=\"my_model\",\n",
    "    model_config={\n",
    "        \"list_params\": [\"v\", \"a\", \"z\", \"t\", \"theta\"],\n",
    "        \"bounds\": {\n",
    "            \"v\": (-3.0, 3.0),\n",
    "            \"a\": (0.3, 3.0),\n",
    "            \"z\": (0.1, 0.9),\n",
    "            \"t\": (0.001, 2.0),\n",
    "            \"theta\": (-0.1, 1.3),\n",
    "        } # bounds will be used to create Uniform (uninformative) priors by default\n",
    "          # if priors are not supplied in `include`.\n",
    "        \"default_priors\": ... # usually no need to supply this.\n",
    "        \"rv\": MyRV # provide a RandomVariable class if pps is needed.\n",
    "    },\n",
    "    loglik=\"my_model.onnx\", # Can be a path to an onnx model.\n",
    "    loglik_kind=\"approx_differentiable\",\n",
    "    include=[...]\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supported types of likelihoods\n",
    "\n",
    "When default likelihoods are not used, custom likelihoods are supplied via `loglik` argument to `HSSM`. Depending on what `loglik_kind` is used, `loglik` supports different types of Python objects:\n",
    "\n",
    "- `Type[pm.Distribution]`: Supports all `loglik_kind`s.\n",
    "\n",
    "  You can pass any **subclass** of `pm.Distribution` to `loglik` representing the underlying top-level distribution of the model. It has to be a class instead of an instance of the class.\n",
    "\n",
    "- `Op`: Supports all `loglik_kind`s.\n",
    "\n",
    "  You can pass a `pytensor` `Op` (an instance instead of the class itself), in which case HSSM will create a top-level `pm.Distirbution`, which calls this `Op` in its `logp` function to compute the log-likelihood.\n",
    "\n",
    "- `Callable`: Supports all `loglik_kind`s.\n",
    "\n",
    "  You can use any Python Callable as well. When `loglik_kind` is `blackbox`, HSSM will wrap it in a `pytensor` `Op` and create a top-level `pm.Distribution` with it. Otherwise, HSSM will assume that this Python callable is created with `pytensor` and is thus differentiable.\n",
    "\n",
    "- `str` or `Pathlike`: Only supported when `loglik_kind` is `approx_differentiable`.\n",
    "\n",
    "  The `str` or `Pathlike` indicates the path to an `onnx` file which represents the neural network for likelihood approximation. In the case of `str`, if the path indicated by `str` is not found locally, HSSM will also look for the `onnx` file in the official HuggingFace repo. An error is thrown when the `onnx` file is not found.\n",
    "\n",
    "**Note**\n",
    "\n",
    "When using `Op` and `Callable` types of likelihoods, they need to have the this signature:\n",
    "\n",
    "```\n",
    "def logp_fn(data, *):\n",
    "    ...\n",
    "```\n",
    "\n",
    "where `data` is a 2-column numpy array and `*` represents named arguments in the order of the parameters in `list_params`. For example, if a model's `list_params` is `[\"v\", \"a\", \"z\", \"t\"]`, then the `Op` or `Callable` should at least look like this:\n",
    "\n",
    "```\n",
    "def logp_fn(data, v, a, z, t):\n",
    "    ...\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `blackbox` likelihoods\n",
    "\n",
    "HSSM also supports \"black box\" likelihood functions, which are assumed to not be differentiable. When `loglik_kind` is `blackbox`, by default, HSSM will switch to a MCMC sampler that does not use differentiation. Below is an example showing how to use a `blackbox` likelihood function. We use a log-likelihood function for `ddm` written in Cython to show that you can use any function or computation inside this function as long as the function itself has the signature defined above. [See here](https://github.com/brown-ccv/hddm-wfpt/blob/9107e4f1e480afcce2cd3cb7ac2279f8aecb596c/hddm_wfpt/wfpt.pyx#L32-L52) for the function definition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">Slice: [t]\n",
      ">Slice: [a]\n",
      ">Slice: [z]\n",
      "CompoundStep\n",
      ">Slice: [t]\n",
      ">Slice: [a]\n",
      ">Slice: [z]\n",
      ">Slice: [v]\n"
     ]
    }
   ],
   "source": [
    "import hddm_wfpt\n",
    "import bambi as bmb\n",
    "\n",
    "\n",
    "# Define a function with fun(data, *) signature\n",
    "def my_blackbox_loglik(data, v, a, z, t, err=1e-8):\n",
    "    data = data[:, 0] * data[:, 1]\n",
    "\n",
    "    # Our function expects inputs as float64, but they are not guaranteed to\n",
    "    # come in as such --> we type convert\n",
    "    return hddm_wfpt.wfpt.pdf_array(\n",
    "        np.float64(data),\n",
    "        np.float64(v),\n",
    "        0,\n",
    "        np.float64(2 * a),\n",
    "        np.float64(z),\n",
    "        0,\n",
    "        np.float64(t),\n",
    "        0,\n",
    "        err,\n",
    "        1,\n",
    "    )\n",
    "\n",
    "\n",
    "# Create the model with pdf_ddm_blackbox\n",
    "model = hssm.HSSM(\n",
    "    data=data,\n",
    "    model=\"ddm\",\n",
    "    loglik=my_blackbox_loglik,\n",
    "    loglik_kind=\"blackbox\",\n",
    "    model_config={\n",
    "        \"bounds\": {\n",
    "            \"v\": (-10.0, 10.0),\n",
    "            \"a\": (0.0, 4.0),\n",
    "            \"z\": (0.0, 1.0),\n",
    "            \"t\": (0.0, 2.0),\n",
    "        }\n",
    "    },\n",
    "    t=bmb.Prior(\"Uniform\", lower=0.0, upper=2.0, initval=0.1),\n",
    ")\n",
    "\n",
    "sample = model.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.285</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.326</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1298.0</td>\n",
       "      <td>2066.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.534</td>\n",
       "      <td>0.027</td>\n",
       "      <td>1.484</td>\n",
       "      <td>1.585</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1647.0</td>\n",
       "      <td>2062.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>0.489</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.466</td>\n",
       "      <td>0.511</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1385.0</td>\n",
       "      <td>2394.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>0.509</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1650.0</td>\n",
       "      <td>2386.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "t  0.285  0.022   0.245    0.326      0.001    0.000    1298.0    2066.0   \n",
       "a  1.534  0.027   1.484    1.585      0.001    0.000    1647.0    2062.0   \n",
       "z  0.489  0.012   0.466    0.511      0.000    0.000    1385.0    2394.0   \n",
       "v  0.509  0.029   0.454    0.562      0.001    0.001    1650.0    2386.0   \n",
       "\n",
       "   r_hat  \n",
       "t    1.0  \n",
       "a    1.0  \n",
       "z    1.0  \n",
       "v    1.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 't'}>, <Axes: title={'center': 't'}>],\n",
       "       [<Axes: title={'center': 'a'}>, <Axes: title={'center': 'a'}>],\n",
       "       [<Axes: title={'center': 'z'}>, <Axes: title={'center': 'z'}>],\n",
       "       [<Axes: title={'center': 'v'}>, <Axes: title={'center': 'v'}>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_trace(sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the blackbox interface provides maximum flexibility on the user side. We hope you will find it useful!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hssm_py311",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
